{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c6eec34-ecc8-4ce1-9b50-8859bda466a3",
   "metadata": {},
   "source": [
    "Ch13 聊聊NumPy\n",
    "# 答案\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c984ec48-5cb3-412c-b581-6d1d6fe29582",
   "metadata": {},
   "source": [
    "## Q1. 用至少两种办法生成一个3 × 4二维NumPy数组，数组的每个值都是10。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "450697ae-31cd-454f-8e58-5376c9b983e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10, 10],\n",
       "       [10, 10, 10, 10],\n",
       "       [10, 10, 10, 10]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.array([[10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10]])\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7c3c0cb-1bd6-4356-b21d-82f5817e4112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_1 = np.ones((3, 4)) * 10\n",
    "array_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a3346e4-b8ea-4be6-9e4c-044e42f5af2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2 = np.zeros((3, 4)) + 10\n",
    "array_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f1cbd37-98d2-4c3c-b092-e2a6ad999971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10, 10],\n",
       "       [10, 10, 10, 10],\n",
       "       [10, 10, 10, 10]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_3 = np.full((3, 4), 10)\n",
    "array_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6810f0d-7927-432a-9364-339638b4a663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_4 = np.linspace(10,10,12).reshape((3,4))\n",
    "array_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28e1d09e-3087-467d-b548-c8b7b56d8543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10, 10],\n",
       "       [10, 10, 10, 10],\n",
       "       [10, 10, 10, 10]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_5 = np.tile(10, (3, 4))\n",
    "array_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ad9538b-79bc-4598-8294-ac723e35b290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10, 10],\n",
       "       [10, 10, 10, 10],\n",
       "       [10, 10, 10, 10]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_6 = np.repeat([10], 12).reshape(3, 4)\n",
    "array_6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afdf4949-7460-4f48-bbec-db5e1f49543a",
   "metadata": {},
   "source": [
    "## Q2. 利用numpy.meshgrid() 和 matplotlib.pyplot.contour() 绘制二元函数 $ f(x_1, x_2) = x_2\\exp(-x_1^2 - x_2^2)$ 的平面等高线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe4620cc-edae-46de-b05d-a8d8dabb5a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_array = np.linspace(-3,3,121)\n",
    "x2_array = np.linspace(-3,3,121)\n",
    "\n",
    "xx1,xx2 = np.meshgrid(x1_array, x2_array)\n",
    "\n",
    "ff = xx2 * np.exp(-xx1**2 - xx2**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "26f547d6-fd3f-405b-80b8-a07e69677dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (5,5))\n",
    "\n",
    "CS = ax.contour(xx1, xx2, ff,\n",
    "           levels = 20,\n",
    "           cmap = 'RdYlBu_r')\n",
    "fig.colorbar(CS)\n",
    "\n",
    "ax.set_xlabel('$\\it{x_1}$')\n",
    "ax.set_ylabel('$\\it{x_2}$')\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax.set_xlim(xx1.min(), xx1.max())\n",
    "ax.set_ylim(xx2.min(), xx2.max())\n",
    "ax.grid(False)\n",
    "ax.set_aspect('equal', adjustable='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb40bd9-8f39-4933-91ce-06d50d2786e9",
   "metadata": {},
   "source": [
    "## Q3. 在 [0, 1] 范围内生成1000个满足连续均匀随机数，并用matplotlib.pyplot.hist()绘制频率直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5f9493c8-6206-4265-a416-f5f8d13038d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.02272282, 1.0427762 , 1.01269612, 0.90240248, 1.15306984,\n",
       "        0.96256265, 0.98261604, 0.90240248, 0.90240248, 1.14304315]),\n",
       " array([1.09342818e-04, 9.98431108e-02, 1.99576879e-01, 2.99310647e-01,\n",
       "        3.99044415e-01, 4.98778183e-01, 5.98511951e-01, 6.98245719e-01,\n",
       "        7.97979487e-01, 8.97713255e-01, 9.97447023e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.random.uniform(0,1,1000)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (5,5))\n",
    "ax.hist(X, 10, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9961fd41-cc0c-492c-8b8e-d65ca02a8560",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repo: https://github.com/Visualize-ML\n",
    "# Book 2 Beauty of Visualization  |  From Basic Arithmetic to Machine Learning\n",
    "# Published and copyrighted by Tsinghua University Press\n",
    "# Beijing, China, 2023"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
